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|Title: ||Latent Spaces for Dynamic Movement Primitives|
|Authors: ||Bitzer, Sebastian|
|Issue Date: ||2009|
|Journal Title: ||Proc. 9th IEEE RAS International Conference on humanoid Robots (Humanoids ’09)|
|Abstract: ||Dynamic movement primitives (DMPs) have been
proposed as a powerful, robust and adaptive tool for planning
robot trajectories based on demonstrated example movements.
Adaptation of DMPs to new task requirements becomes difficult
when demonstrated trajectories are only available in joint
space, because their parameters do not in general correspond
to variables meaningful for the task. This problem becomes
more severe with increasing number of degrees of freedom and
hence is particularly an issue for humanoid movements. It has
been shown that DMP parameters can directly relate to task
variables, when DMPs are learned in latent spaces resulting
from dimensionality reduction of demonstrated trajectories.
As we show here, however, standard dimensionality reduction
techniques do not in general provide adequate latent spaces
which need to be highly regular.
In this work we concentrate on learning discrete (point-topoint)
movements and propose a modification of a powerful
nonlinear dimensionality reduction technique (Gaussian Process
Latent Variable Model). Our modification makes the GPLVM
more suitable for the use of DMPs by favouring latent spaces
with highly regular structure. Even though in this case the
user has to provide a structure hypothesis we show that its
precise choice is not important in order to achieve good results.
Additionally, we can overcome one of the main disadvantages
of the GPLVM with this modification: its dependence on the
initialisation of the latent space. We motivate our approach on
data from a 7-DoF robotic arm and demonstrate its feasibility
on a high-dimensional human motion capture data set.|
|Appears in Collections:||Informatics Publications|
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